Exploring health promotion efforts for non-communicable disease prevention and control in Ghana
Bibliographic record
Abstract
Noncommunicable diseases (NCDs) are a growing public health challenge in Ghana. Health promotion can provide useful avenues to reduce the incidence of NCDs in the country. We used the Ottawa Framework to assess health promotion efforts for the prevention and control of NCDs in Ghana. Data were collected using key informant interviews and documentary sources. A content analysis approach was adopted for data analysis using Nvivo 11 Software. We found a strong policy framework for NCD prevention in Ghana with the ratification of several international protocols and resolutions and the development of national and specific NCD-related policies. Implementation of these policies, however, remains achallenge due to limited resources and the overconcentration on communicable diseases. Attempts have been made to create a supportive environment through increased access to NCD services but there are serious challenges. Respondents believe the current environment does not support healthy eating and promotes unhealthy use of alcohol. The Community-based Health Planning and Services (CHPS) program engenders community participation in health but has been affected by inadequate resources. Personal skills and education programs on NCDs are erratic and confined to a few municipalities. We also found that NCD services in Ghana continue to be clinical and less preventative. These findings have far-reaching implications for practice and require health planners in Ghana to pay equal attention in terms of budgetary allocations and other resources to both NCDs and communicable diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".